As the forensic community is transitioning to probabilistic genotyping and the use of likelihood ratios to assign probative weight to DNA mixtures, the assessment of the number of contributors (NOC) needs to be more robust for mixture interpretation. However, NOC assessment can be challenging for low-template and/or high order mixtures. Here, we present a quick and easy-to-use tool to help with NOC estimation: total allele count curves (TAC curves). TAC curves for two to seven contributors were generated using sets of 20,000 in silico mixtures, for five populations (African American, Caucasian, Asian, Apache and Native Alaska) and for commonly used commercial STR kits (GlobalFiler^TM, PowerPlex® Fusion, PowerPlex® ESX 17 and Identifiler^TM). To assess the performance of TAC curves, the NOC was evaluated for 80 mixtures, with and without use of the curves. Results show that TAC curves allow for a better NOC assessment as correct evaluations rose from 10% when using maximal allele count (MAC) to 65% when also using TAC for four to six contributor mixtures. Supplemental data for this article is available online at http://dx.doi.org/10.1080/00085030.2022.2028359 .

As the forensic community is transitioning to probabilistic genotyping and the use of likelihood ratios to assign probative weight to DNA mixtures, the assessment of the number of contributors (NOC) needs to be more robust for mixture interpretation. However, NOC assessment can be challenging for low-template and/or high order mixtures. Here, we present a quick and easy-to-use tool to help with NOC estimation: total allele count curves (TAC curves). TAC curves for two to seven contributors were generated using sets of 20,000 in silico mixtures, for five populations (African American, Caucasian, Asian, Apache and Native Alaska) and for commonly used commercial STR kits (GlobalFiler^TM, PowerPlex® Fusion, PowerPlex® ESX 17 and Identifiler^TM). To assess the performance of TAC curves, the NOC was evaluated for 80 mixtures, with and without use of the curves. Results show that TAC curves allow for a better NOC assessment as correct evaluations rose from 10% when using maximal allele count (MAC) to 65% when also using TAC for four to six contributor mixtures. Supplemental data for this article is available online at http://dx.doi.org/10.1080/00085030.2022.2028359 .

As the forensic community is transitioning to probabilistic genotyping and the use of likelihood ratios to assign probative weight to DNA mixtures, the assessment of the number of contributors (NOC) needs to be more robust for mixture interpretation. However, NOC assessment can be challenging for low-template and/or high order mixtures. Here, we present a quick and easy-to-use tool to help with NOC estimation: total allele count curves (TAC curves). TAC curves for two to seven contributors were generated using sets of 20,000 in silico mixtures, for five populations (African American, Caucasian, Asian, Apache and Native Alaska) and for commonly used commercial STR kits (GlobalFiler^TM, PowerPlex® Fusion, PowerPlex® ESX 17 and Identifiler^TM). To assess the performance of TAC curves, the NOC was evaluated for 80 mixtures, with and without use of the curves. Results show that TAC curves allow for a better NOC assessment as correct evaluations rose from 10% when using maximal allele count (MAC) to 65% when also using TAC for four to six contributor mixtures. Supplemental data for this article is available online at http://dx.doi.org/10.1080/00085030.2022.2028359 .